docstr: Forward function.
  
  Args:
      pred (torch.Tensor): The prediction, has a shape (n, *).
      target (torch.Tensor): The label of the prediction,
          shape (n, *), same shape of pred.
      weight (torch.Tensor, optional): The weight of loss for each
          prediction, has a shape (n,). Defaults to None.
      avg_factor (int, optional): Average factor that is used to average
          the loss. Defaults to None.
      reduction_override (str, optional): The reduction method used to
          override the original reduction method of the loss.
          Options are "none", "mean" and "sum".
  
  Returns:
      torch.Tensor: The calculated loss
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code: def forward(
      self,
      pred,
      target,
      weight=None,
      reduction_override=None,
      avg_factor=None,
  ):
      """Forward function.
  
      Args:
          pred (torch.Tensor): The prediction, has a shape (n, *).
          target (torch.Tensor): The label of the prediction,
              shape (n, *), same shape of pred.
          weight (torch.Tensor, optional): The weight of loss for each
              prediction, has a shape (n,). Defaults to None.
          avg_factor (int, optional): Average factor that is used to average
              the loss. Defaults to None.
          reduction_override (str, optional): The reduction method used to
              override the original reduction method of the loss.
              Options are "none", "mean" and "sum".
  
      Returns:
          torch.Tensor: The calculated loss
      """
  
      assert reduction_override in (None, "none", "mean", "sum")
      reduction = (
          reduction_override if reduction_override else self.reduction
      )
  
      if self.activate:
          if self.use_sigmoid:
              pred = pred.sigmoid()
          else:
              raise NotImplementedError
  
      if self.naive_dice:
          loss = self.loss_weight * naive_dice_loss(
              pred,
              target,
              weight,
              eps=self.eps,
              reduction=reduction,
              avg_factor=avg_factor,
          )
      else:
          loss = self.loss_weight * dice_loss(
              pred,
              target,
              weight,
              eps=self.eps,
              reduction=reduction,
              avg_factor=avg_factor,
          )
  
      return loss
  
file_path: dinov2/dinov2/eval/segmentation_m2f/models/losses/dice_loss.py
imports: import torch
  import torch.nn
  from mmseg.models.builder import LOSSES
  from mmseg.models.losses.utils import weight_reduce_loss
label_desc: Use image node_id 2 for calling the DiceLoss obj's underlying member method code with example usage: obj.forward(pred, target, weight, reduction_override, avg_factor) and returns: loss